Exploring Healthcare Experiences of Transgender Individuals
Bibliographic record
Abstract
Purpose: It has been widely noted that existing healthcare systems do not always function effectively for the transgender population. Despite existing healthcare barriers, however, transgender individuals have been shown to have positive healthcare experiences. This study explored a cohort of transgender individuals who had positive healthcare experiences, and those who were involved in creating a positive healthcare experience for transgender individuals. Methods: A single case study was conducted, which included 10 interviews with transgender individuals, healthcare providers, and friends/family/significant others of transgender individuals. Data were analyzed through thematic analysis. Results: Seven key themes emerged within macro levels (large-scale system), meso levels (local/interpersonal), and micro levels (individual/internal) of healthcare system support. At a macro level, few system strengths were shown, with hope for change in the future. On a meso level, both external supports and informal networking emerged as key factors in positive healthcare experiences. At the micro level, self-navigation, characteristics for success, and personal strategy development were important for achieving positive experiences. Conclusion: Factors that contribute to positive healthcare experiences for transgender individuals were outlined in this study, showing that meso and micro level support compensate for large-scale healthcare system deficits.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".